In order to build an assistive system for robotic data collection, we need to solve two key challenges: (1) we need to learn assistive policies from diverse human-collected data, which is known to be challenging [1]}, and (2) we need to learn when to ask for operator input while keeping such interventions at a minimum. To address these challenges, we propose to use a hierarchical stochastic policy that can learn effectively from diverse human data. Further, we use the policy's stochastic predictions to estimate its uncertainty about how to act in the current scene and which task to pursue. Then, we use this estimate to elicit operator input only if the assistive policy is uncertain about how to proceed.
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